Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Evolving neural networks through augmenting topologies
Evolutionary Computation
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Efficiently evolving programs through the search for novelty
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Automatically discovering properties that specify the latent behavior of UML models
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Introducing novelty search in evolutionary swarm robotics
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Searching for novel classifiers
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Behavioral repertoire learning in robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Ribosomal robots: evolved designs inspired by protein folding
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Open-ended behavioral complexity for evolved virtual creatures
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Searching for novel clustering programs
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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An ambitious challenge in artificial life is to craft an evolutionary process that discovers a wide diversity of well-adapted virtual creatures within a single run. Unlike in nature, evolving creatures in virtual worlds tend to converge to a single morphology because selection therein greedily rewards the morphology that is easiest to exploit. However, novelty search, a technique that explicitly rewards diverging, can potentially mitigate such convergence. Thus in this paper an existing creature evolution platform is extended with multi-objective search that balances drives for both novelty and performance. However, there are different ways to combine performance-driven search and novelty search. The suggested approach is to provide evolution with both a novelty objective that encourages diverse morphologies and a local competition objective that rewards individuals outperforming those most similar in morphology. The results in an experiment evolving locomoting virtual creatures show that novelty search with local competition discovers more functional morphological diversity within a single run than models with global competition, which are more predisposed to converge. The conclusions are that novelty search with local competition may complement recent advances in evolving virtual creatures and may in general be a principled approach to combining novelty search with pressure to achieve.